CHAIR networking seminar, organized by the AI for Scientific Data Analysis theme.
Overview
- Date:Starts 19 October 2023, 13:15Ends 19 October 2023, 16:00
- Seats available:63
- Location:
- Language:English
- Last sign up date:17 October 2023
Speaker:
Assistant Prof. Mohsen Mirkhalaf
Abstract:
During the last few decades, industries such as aerospace and wind energy (among others) have been remarkably influenced by the introduction of high-performance composites.
One challenge, however, for modeling and designing composites is the lack of computational efficiency of accurate high-fidelity models. For design purposes, using conventional optimization approaches typically results in cumbersome procedures due to huge dimensions of the design space and high computational expense of full-field simulations.
In recent years, deep learning techniques have been found to be promising methods to increase the efficiency and robustness of a variety of algorithms in multi-scale modeling and design of composites.
In this presentation, I will talk about our recent activities in usage of artificial neural networks and micromechanical simulations to develop significantly fast and highly accurate models for composite materials.
![](/_next/image/?url=https%3A%2F%2Fcms.www.chalmers.se%2FMedia%2Fdijpexcy%2Frod.jpg%3Fwidth%3D600%26height%3D600%26v%3D1d99247e5d5d5f0%26quality%3D60%26format%3Dwebp&w=3840&q=90)
AI for Scientific Data Analysis
This theme is about utilizing the power of AI as a tool for scientific research. AI can be applied to, and potentially speed up, discovery and utilization in a variety of research disciplines, such as microscopy, physics, biology, chemistry, and astronomy.